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dc.contributor.authorMondal, Riaz
dc.contributor.authorRistaniemi, Tapani
dc.contributor.authorTurkka, Jussi
dc.date.accessioned2017-12-18T10:47:55Z
dc.date.available2017-12-18T10:47:55Z
dc.date.issued2015
dc.identifier.citationMondal, R., Ristaniemi, T., & Turkka, J. (2015). Genetic Algorithm Optimized Grid-based RF Fingerprint Positioning in Heterogeneous Small Cell Networks. In <i>Proceedings of 2015 International Conference on Localization and GNSS (ICL-GNSS)</i>. IEEE. International Conference on Localization and GNSS. <a href="https://doi.org/10.1109/ICL-GNSS.2015.7217160" target="_blank">https://doi.org/10.1109/ICL-GNSS.2015.7217160</a>
dc.identifier.otherCONVID_25444964
dc.identifier.otherTUTKAID_68613
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/56393
dc.description.abstractIn this paper we propose a novel optimization algorithm for grid-based RF fingerprinting to improve user equipment (UE) positioning accuracy. For this purpose we have used Multi-objective Genetic Algorithm (MOGA) which enables autonomous calibration of gridcell layout (GCL) for better UE positioning as compared to that of the conventional fingerprinting approach. Performance evaluations were carried out using two different training data-sets consisting of Minimization of Drive Testing measurements obtained from a dynamic system simulation in a heterogeneous LTE small cell environment. The robustness of the proposed method has been tested analyzing positioning results from two different areas of interest. Optimization of GCL is performed in two ways: (1) array-wise calibration of the grid-cell units using non-overlapping GCL and (2) creating an overlapping GCL to cover of whole simulation area with different rectangular grid-cell units. Simulation results show that if sufficient amount of training data is available then the proposed method can improve positioning accuracy of 56.74% over the conventional gridbased RF fingerprinting.
dc.language.isoeng
dc.publisherIEEE
dc.relation.ispartofProceedings of 2015 International Conference on Localization and GNSS (ICL-GNSS)
dc.relation.ispartofseriesInternational Conference on Localization and GNSS
dc.subject.othergrid-based RF fingerprinting
dc.subject.otherminimization of drive tests
dc.subject.othermulti-objective genetic algorithm
dc.subject.otherKullback-Leibler divergence
dc.titleGenetic Algorithm Optimized Grid-based RF Fingerprint Positioning in Heterogeneous Small Cell Networks
dc.typeconferenceObject
dc.identifier.urnURN:NBN:fi:jyu-201712114596
dc.contributor.laitosTietotekniikan laitosfi
dc.contributor.laitosDepartment of Mathematical Information Technologyen
dc.contributor.oppiaineTietotekniikkafi
dc.contributor.oppiaineMathematical Information Technologyen
dc.type.urihttp://purl.org/eprint/type/ConferencePaper
dc.date.updated2017-12-11T07:15:20Z
dc.relation.isbn978-1-4799-9858-6
dc.type.coarconference paper
dc.description.reviewstatuspeerReviewed
dc.relation.issn2325-0771
dc.type.versionacceptedVersion
dc.rights.copyright© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.rights.accesslevelopenAccessfi
dc.relation.conferenceInternational conference on localization and GNSS
dc.relation.doi10.1109/ICL-GNSS.2015.7217160


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